Research Organization of Information and Systems

The Institute of
Statistical Mathematics
2019 -2020

The Institute of Statistical Mathematics

C O N T E N TS
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■ Institute Overview—2■ Research Centers ——————————————

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NOE ( Network Of Excellence ) Project
Project for Fostering and Promoting
Statistical Thinking

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■ History—

Message from Director-General

I am Tsubaki Hiroe and I have taken office as the 12th Director-General of theInstitute of Statistical Mathematics. While I carry on the tradition of the Institute that my predecessors have established and passed on, I intend to ensurethat the Institute fulfill every role it is expected to play in society.
Today, the whole world is searching for the ideal state of data-driven social
activities that utilize data as fundamental resources. Accordingly, people’s
lives are also rapidly changing. As a consequence, activities that generate intellectual and economic values using statistics as its core academic basis now
have influence not only on the progress of various metric and mathematicalsciences for academic researchers but also over social and economic activities for the public. It is not an exaggeration to say that a new era has come,where we should place statistical mathematics at the core of our society.
The Institute of Statistical Mathematics is the only research institute in Japan for statistical science and related mathematical sciences. Although I must
admit that the Institute is not large enough to take full responsibility for such
revolutionary social changes, we still have an important mission to perform,
precisely because the Institute is open to all universities for joint research.
Our task is to create a cooperative network to help promote the development and social expansion of so-called“data science,” which focuses on the process of generating intellectual value. Furthermore, we have to establish acontinuous management cycle of the network that the Institute and its collaborators have created, which should be
evaluated by the degree of contribution to the academic world, a data-driven society, and the development of thenext-generation of human resources. This is the mission that has been carried out by my predecessors, and I am
humbly taking on the task at my own discretion.
This task can be formulated in my Director-General’s policy as follows.
First, our goal is to create a research system where we provide joint support with researchers both in Japan andabroad to study statistical mathematic sciences that support each phase of the process for generating intellectual
value as well as the common elements that underlie these phases. Through this system, we intend to deepen our
understanding of basic mathematical science for data science. To this end, I will work with related communities tocreate a roadmap for statistical sciences, mathematic sciences, and basic mathematics for data science.
Second, I intend to determine how statistical mathematics, statistical mathematic sciences, and other academicareas should be integrated into the generative process of intellectual value in cooperation with prominent data
scientists from the government, industry, and academia. Through this mechanism, I plan to help advance the academic and industrial world in Japan to make them ready for a data-driven society. To realize this, I will develop bothan international and a domestic network within the NOE (network of excellence) project. While the international
network aims to deepen cross-disciplinary research through core mathematics researchers, the domestic network
aims to develop core human resources of statistical mathematics among intra-disciplinary researchers and businessmen and advance domestic intra-disciplinary activities. Given these clearly distinctive aims, the two networks
should be able to exchange information whenever necessary and develop together into efficient networks.
Third, I plan to create a mechanism where industry, government, and academia collaboratively cultivate worldclass data scientists who can provide a foundation for the data science era, from the next generation of researchers,or the general public. Through this mechanism, we can develop a substantial stratum of professional data scientists and thereby contribute to the advancement of the data-driven era. As a first step, I intend to identify educatorsand their activities in developing statistical human resources in Japan.
In order to carry out these plans and meet the expectations of our future society, nothing is more important thancreative and spontaneous activities performed by each staff member at the Institute. My main duty as DirectorGeneral is to encourage such initiative. The growth of our staff through self-realization is the most important resource for the development of the Institute and the communities that support us.
The Institute of Statistical Mathematics marked its 75th anniversary in June of this year. I truly hope that, by the100th anniversary of the Institute, small ripples of our academic and social contributions will make big positive
waves for the people in a data-driven society and that all our staff will do their best to realize that goal. I appreciate
your continued guidance and encouragement.

Hiroe Tsubaki

Director-General
The Institute of Statistical Mathematics1Institute Overview

Basic Research
Department of Statistical Modeling
The Department of Statistical Modeling works on structural modeling of physical phenomena related to numerous factors,
and conducts research on model-based statistical inference methodologies. By means of model-based prediction and control,modeling of complex systems, and data assimilation, the department aims to contribute to the development of modeling intel­
ligence in many fields.

■ Prediction and Control Group ————————————————————————————————————————————The Prediction and Control Group works on the development and evaluation of statistical models, which function effectively
in terms of prediction and control of phenomena, decision making, and scientific discoveries. These efforts involve data
analysis and modeling related to phenomena that vary across time and space.

■ Complex System Modeling Group —————————————————————————————————————————The Complex System Modeling Group conducts studies aimed at discovering the structures of complex systems, such as
nonlinear systems and hierarchical networks, through statistical modeling. For these purposes, the group also considers
Monte Carlo simulations, discrete mathematics, and computer science.

■ Data Assimilation Group ———————————————————————————————————————————————The Data Assimilation Group works on the development of data assimilation techniques, which are procedures aimed
at combining information derived from large amounts of observations and a numerical simulation model. By developing
computational algorithms and high-performance parallel computing systems, the group aims to build a next-generation
simulation model that can predict the future in real time.

Department of Statistical Data Science
The Department of Statistical Data Science conducts research on data design methods aimed at managing uncertainty
and incompleteness of information, quantitative methods for evidence-based practice, and related data analysis methods.
Moreover, the department investigates methods for inferring the latent structures in target phenomena from observation data.

■ Survey Science Group ————————————————————————————————————————————————The Survey Science Group promotes research on the design of statistical surveys, development of statistical analysis
methods on survey data, and applications. By exploring complex phenomena in various fields, the group also aims tocontribute to practical applications in academia and policy-making through social surveys.

■ Metric Science Group —————————————————————————————————————————————————The Metric Science Group conducts research aimed at identifying and evaluating statistical evidence through quan­tification of phenomena that have not been measured thus far, as well as efficient information extraction from large
databases. The group investigates related methods and develops methods for analyzing the collected data. By working
on applied research in various fields of real science, the group aims to advance practical, applied, statistical mathematicalresearch based on evidence.

■ Structure Exploration Group —————————————————————————————————————————————The Structure Exploration Group conducts research on statistical science aimed at inferring the latent “structure” behindvarious target phenomena in biology, physics, and social science, based on observational data. The group focuseson machine learning, Bayesian reasoning, experimental design methods, and spatial-temporal analysis methods toinvestigate micro/meso/macroscopic and spatial-temporal dynamic structures in target phenomena.

Department of Statistical Inference and Mathematics
The Department of Statistical Inference and Mathematics carries out research into general statistical theory, statisticallearning theory, optimization, and algorithms for statistical inference.

■ Mathematical Statistics Group ———————————————————————————————————————————The Mathematical Statistics Group is concerned with aspects of statistical inference theory, modeling of uncertain
phenomena, stochastic processes and their application to inference, probability and distribution theory, and the related
mathematics.

■ Learning and Inference Group ————————————————————————————————————————————The Learning and Inference Group develops statistical methodologies to describe the stochastic structure of data math­ematically and clarify the potential and the limitations of the data theoretically.

■ Mathematical Optimization Group —————————————————————————————————————————The Mathematical Optimization Group focuses on mathematical theory and practical applications of optimization andcomputational algorithms together with underlying numerical or functional analysis and discrete mathematics.

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NOE-type Research
Risk Analysis Research Center
Risk Analysis Research Center is pursuing a scientific approach to managing uncertainties and risks in society, which haveincreased with the growing globalization of society and economy. Our research projects are mainly seismology, finance,
resources, environmentology, database development and risk mathematics. The Center also manages a network organization
for risk analysis to facilitate research collaboration across different organizations with the common goal of creating a safe andresilient society.

Research Center for Statistical Machine Learning
Machine learning is a research field associated with autonomous systems that can learn their behavior from data. This field
is based on both the statistical science concerning inference from data and computer science concerning efficient algorithms.
It can be applied to broad disciplines ranging engineering and information science to natural science, such as robotics andbrain sciences. Our research center aims at supporting the academic community of this field, as well as producing influential
research through various joint projects.

Data Science Center for Creative Design and Manufacturing
We aim to foster new scientific methods for innovative design and manufacturing. Various fields in manufacturing are now
facing a revolutionary period. Population reduction and globalization are bringing dramatic changes in the industrial structure
in Japan. Countries around the world has actively developed their growth strategies utilizing data science as a driving force.
We have accumulated state-of-the-art technologies in data science here. We are devoted to foster and practice advanced
methods in data science for design and manufacturing.

Research Center for Medical and Health Data Science
Research Center for Medical and Health Data Science aims to facilitate statistical data science research that covers
medical studies, drug developments, health care, and public health. Our research projects involve fundamental mathematics
and compu­tational science for medical applications, applied methodology for basic, clinical and social medicine, and modern
technology such as artificial intelligence, machine learning, and big data analyses. Furthermore, our research center aims at
constructing a research network of the academic community of this field, as well as offering advanced statistical education
programs.

Professional Development
School of Statistical Thinking
The mission of the School of Statistical Thinking is to plan and implement various programs for statistical thinking, fromextension courses to a professional development program. The researchers affiliated with the school are often involved withspecific data analysis projects, which help them to gain hands-on knowledge of data science. We expect such an experienced
researcher will play an active role as a number-cruncher, as a modeler, or as a project coordinator.

Research Support
Center for Engineering and Technical Support
The Center for Engineering and Technical Support assists academics and their collaborators in many ways: managing
computer systems and networks, editing and publishing journals, maintaining the library, and managing tutorial programs.

■ Computing Facilities Unit The Computing Facilities Unit is in charge of managing computer facilities and scientificsoftware.

■ Computer Networking Unit The Computer Networking Unit is responsible for computer networking and its infra­
structure, and network security.

■ Information Resources Unit The Information Resources Unit is responsible for maintaining a library and an electronic
repository, and is in charge of planning statistical tutorial programs open to the public.

■ Media Development Unit The Media Development Unit is in charge of publishing and editing of research results
and PR brochures.

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Research Centers
Risk Analysis Research Center

Computational Algebra and Sampling■ Motivating problems in data analysis
The book entitled “Environmental Statistics” written by Vic Bernett consists of five parts: extremes
and outliers, sampling and monitoring, stimulus andresponse, standards and regulations, and spatial
and temporal processes. They may be chosen to present typical problems emerging in data analysis at
the interface of statistical methods and environmental subjects. In this article, with keeping these problems in mind, a research activity of application ofcomputational algebra to data analysis is introduced.
■ Toric modelA standard statistical model of count data is thetoric model, where the logarithm of the probability
that an observation falls into a cell is given by a linear model of marginal effects and interactions, where
each term corresponds to a face of a simplicial simplex. A model whose facets correspond to the maximal cliques of a graph is called a graphical model.
Figures 1 and 2 are the graphical model of the twoway contingency table with independence and agraphical model, respectively. In Figure 1, the facets
are the two vertexes corresponding to the rows andcolumns.
■ SamplerLet us call the probability that a data has the sameas or smaller than the probability of an observed

Figure 1: The two-way contingency table with independence{ ø, { 1 }, { 2 } }.

4data from a model the tail probability. In statisticaltest, a model is rejected if the tail probability is small.
For example, we test independence of stimulus andresponse by setting that the rows of a contingency
table as the stimulus and the columns as the response. If a closed expression of the tail probability
is not available, it is estimated by simulated datafrom the model. The simulator is called sampler andevaluation of the tail probability is the typical use.
Sampling is straightforward if a model is simple as
Figure 1. However, graphical models as Figure 2 do
not have a good property, and it has been considered that sampling from them is difficult.

■ Computational algebra and sampling
In these 20 years, application of computational algebra to statistical science has been a hot topic. Fora toric model, it was shown that approximate samplers with Markov chain Monte Calro can be constructed by using Gröbner basis of the polynomial
ring and such samplers have been constructed forvarious models. However, the author showed that inprinciple sampling from any toric model is possible
by using Gröbner basis of differential operators. The
details are available in the book “Partitions, Hypergeometric Systems and Dirichlet Processes in Statistics” published in 2018 from Springer.
Shuhei Mano

Figure 2: A graphical model{ ø, { 1 }, { 2 }, { 3 }, { 4 }, { 5 }, { 1, 2 }, { 2, 3 }, { 3, 4 }, { 1, 4 },{ 2, 5 }, { 3, 5}, { 2, 3, 5 }} .

Modeling of Recurrent Earthquakes and Long-Term Earthquake Forecast■ Long-term forecast of earthquakes on
active faults
There are so many active faults in Japan whichhave risks of earthquake disasters in the future. Although it is difficult to foretell earthquake occurrences so far, earthquakes in each active fault recur quasiperiodically and so their probability forecasts canbe evaluated via statistical models. However, mostactive faults in Japan have only a few specified historical events, respectively, due to very long duration of earthquake recurrences. Such a small numberof historical events may cause large errors in parameter inference. Especially, maximum likelihood estimates of aperiodicity parameters (coefficients of variation) in earthquake recurrence intervals havebiases and may make significant biases in probability
forecasts as well. Thus, we propose a Bayesian model to improve stability of parameter inference andpredictive performance. Figure 1 shows estimates ofaperiodicity parameters for respective active faults
and we can find some spatial trends that, for example,

the estimates are relatively high in the center of Honshu island. Furthermore, when we compare the estimates with the active fault map in the right panel of
Figure 1, we can find some correlation between theaperiodicity parameters and fault densities. Considering such spatial trends, we are challenging to improve long-term forecasts of active faults.

■ Monitoring of plate motion based on repeating
earthquake activity
In recent years, development of seismic observation network enabled us to detect small repeating
earthquakes on the same fault patch as well as large
earthquakes on active faults. Especially, small earthquakes repeating on subduction zones of crustal
plates attract much attentions as a key of earthquake
prediction. Temporal accelerations of interplate slip
rate called slow slips are often observed before megathrust earthquakes and can be monitored fromchanges in small repeating seismicity. Thus, we propose a new spatio-temporal stochastic model to estimate slip rate from repeating earthquakes onplate boundaries. Figure
2 shows spatio-temporalvariation of slip rates estimated from small repeating earthquakes on
the subduction zone ofPacific plate from 2008 to
the 2011 Tohoku earthquake. Accelerated subduction can be seen in awide region around thecoseismic area of 2011
Figure 1: Estimates of aperiodicity parameters ( left ) and inland active fault map ( right ).
Tohoku earthquake. Using the proposed model,
we are monitoring potential precursory changes
in interplate slip rate forhuge earthquakes.
Shunichi Nomura

Figure 2: Estimated space–time distribution of interplate slip rate.

5Research Center for Statistical Machine Learning

Inducing “Motion” for Robots by a Statistical Learning■ Relationship between robots and statistical model
As artificial intelligence based on statistical machine learning, i.e. AI, is changing the society, robotics
is considered to be increasingly important in the future as one of the most important applications of machine learning. Robots are often thought to be simply
operating mechanically, but in order to realize sophisticated intelligent operations on them, statistical models that can properly handle various uncertainties andinferences are necessary.

■ Learning “motion” for robots
As opposed to industrial robots that perform fixed
movements, robots that cooperate with people to help
them need to move in the same way as humans do andadjust their behavior appropriately. For robots, "motion" is nothing but the action of their motors, that is,
the time series of the variation vectors of each joint(Figure 1). However, from the time series humans recognize motions such as “walking”, “pressing” and “lifting”. It is easy to understand if you think of a case ofballet. A ballet dance actually consists of a series ofbasic motions. In order to control movements at a highlevel, it is necessary to automatically recognize these“motions” that correspond to “words” in natural language.

■ Gaussian process time series model andnatural language
However, we do not hear from anyone what “walking” is. This is the same as knowing a word in agglutinative language, such as Chinese and Japanese, even if
no one tells you that it is a word. In order to learn “motions” from data autonomously, we leverage a modelfor natural language (Mochihashi et al., 2009; Uchiumi
et al., 2015) which statistically learns “words” purely
from character strings of a language. Based on this, wehave developed a statistical model to learn “motions”
from a time series of various joint angle vectors, in collaboration with the University of Electro-Communications, Ochanomizu University, and the University ofTokyo.
From the viewpoint of statistics, this is a hidden
semi-Markov model with Gaussian process output distributions which is estimated by a Bayesian forwardbackward algorithm. By simultaneously estimating thenumber of hidden states, i.e. how many
“motions” there are, through a hierarchical Dirichlet process, it becomes
possible to learn “motions” with highaccuracy without any supervision(Figure 2). The result of this research
was presented in the top international
conference on robotics, IROS (Nagano
et al., 2018).

Figure 1: Sequence data corresponding to operations. We do not know in advance
where and from where the unit of “motion” corresponds to.

Figure 2: Recognition of “motion” by Gaussian process hidden semi-Markov model. Here, from the time series of the coordinates of the limbs, the “motion”
and the number of their types are automatically inferred.

6■ Application to other areas
This basic technology that can learn“motion” from the time series of sensors at each joint has a wide range ofpossibilities besides robotics. For example, in joint research with the National Center of Neurology and Psychiatry, the application of this technology
to the operation of a small ape, marmoset, will discover how “motions”
will change in response to brain damage or diseases (to appear at JSAI2019). Also, in dance science, I thinkthat it will lead to automatic transcription and statistical modeling of dancescores.
Daichi Mochihashi

Research Centers

Dipole Source Localization from EEG Signal By Particle Filter■ Dipole estimation from EEG
In order to clarify the mechanism of the brain, diagnosis and treatment of diseases such as epilepsy, it isnecessary to understand activity in the brain, andmeans to identify the activation position in the brain
from biological signals is required. When nerve cells
are activated, local currents are generated aroundthem. Measurement of this local current on the scalp is
EEG (electroencephalogram). It can be measured noninvasively and conveniently, and it has high temporal
resolution, so it is being studied as a measurement
method suitable for clinical application (Figure 1).

■ Problem formulation
As a method of identifying the active site in thebrain from the EEG data, a method of replacing a bundle of local current in the brain with a current dipole,
which is a 6-dimensional physical quantity having position and moment, is widely used. Using a model thattakes into consideration the thickness and conductivity of the brain, skull, scalp, it is possible to calculate
the potential (EEG) generated on the scalp by dipolesin the brain. However, determining the position, moment, and number of dipoles from the measured EEG ismathematically a difficult problem called inverse problem.

■ Approach
We formulated the dipole estimation problem frompotential
generationelectrode

EEG as a state space model (Figure 2) and applied aBayesian inference algorithm called particle filter.
Specifically, at each point in time, approximating theprobability distribution of the dipole position / moment,
which is an unobservable internal state, with “ensemble of particles”. Although the dipole estimation method using such a particle filter has been proposed sofar, the methodology for objectively determining thenumber of dipoles has not been sufficiently studied.

■ Information criterion and empirical Bayes method
We have developed a measure of validity of a model called information criterion on the premise that it
is used in combination with a particle filter and toselect the number of dipoles at each time. We also
considered a process in which dipoles are generated
or annihilated in the brain, and developed a method
to estimate the number of dipoles by a method calledempirical Bayes method (Figure 3). We applied thedeveloped method to the actual data, results consistent with neurophysiological findings were obtained.
Currently, we estimate the noise mixed in the time
evolution process of the measurement system andparticle by the framework of Bayesian inference torealize more accurate dipole estimation. We are aiming to applications such as clarify unknown brain
function, brain computer interface, brain disease diagnosis.
Hideitsu Hino

scalp
skull

nerve cell

cerebralcortexaxons

Figure 1: Mechanism of EEG.

Figure 3: Process of estimating number/location/momentum of
dipoles.

Figure 2: State space model.

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Data Science Center for Creative Design and Manufacturing

Data Science Center for Creative Design and Manufacturing■ Mission statement
We aim to foster new scientific methodologies forinnovative design and manufacturing. Various fields inmanufacturing are now facing a revolutionary period.
Population reduction and globalization are bringing
dramatic changes in the industrial structure in Japan,
resulting in a rapid loss of global predominance in industry. Countries around the world has actively developed their growth strategies utilizing data science as adriving force, such as the Materials Genome Initiative in
the US and the Industry 4.0. Following the global trend
is no longer an effective way to survive in the intensive
power game around the world. In 2017, the Institute of
Statistical Mathematics has established a new research
center - Data Science Center for Creative Design and
Manufacturing. We have accumulated state-of-the-art
technologies in data science here, for instance, machine learning, Bayesian inference, materials informatics, and so on. We are devoted to foster and practice
innovative methods in data science for design and manufacturing through industry-academia collaboration.

■ Smart manufacturing
The developments of new materials depend largely
on intuitions of highly experienced professionals, andtime-consuming trial-and-error processes for laboratory synthesis and testing of designed materials basedon computer simulation and experiments. On the other hand, recently, there are new attempts to substitutecomputational or real experiments in materials synthesis and testing by statistical models trained on given data. The enormous cost and time required in thecharacterization of material structures and physicalproperties has limited material studies within a small

Figure: Software developments for materials informatics.

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set of candidates. For example, it has been proved thathigh throughput screening using techniques in data
science significantly increases the chance of discovering innovative functional materials. This is a basic concept of smart manufacturing in the perspective of data
science.

■ Creative design and manufacturing
We recognize the importance of being at the absolute leading edge position in the manufacturing industry. This cannot be done by data science alone. Most of
the classical data science analysis tools are designed
for interpolating predictions. Data science used to be ascience of predictions based on pattern recognition
from existing data. For example, we often assume thatmaterials with similar chemical structure exhibit similar physical properties. However, by definition, a newmaterial is not likely to be similar to any of the existingmaterials. Combination of experiment, theory anddata science methods is an essential step to a new
breakthrough in the current state. In other words, we
adopt a stepwise approach to expand the region of accurate prediction of a statistical model. We achieve thegoal by careful design of an optimal experiment or simulation schedule for new data points that efficiently improve an existing predictive model. We have accomplished preliminary success in materials science using
an extrapolating prediction method based on a data
science approach, and fostered new collaboration opportunities between academia and industry for the
purpose of materials discovery. Our next step is to extend the application to various fields related to creative design and manufacturing.
Ryo Yoshida

Research Centers

XenonPy: The Key Foundation for New Generation Materials Science■ Current status of materials informatics
Materials Genome Initiative, which began in 2011
under the presidency of Barack Obama, has triggered
the Materials Informatics (MI) boom, i.e., the concept
of using data science and machine learning methods
to accelerate the discovery of high performance novel materials. By mapping the highly diverse materials
space into a set of “descriptors” and developing agenerator of material candidates, we are able to exploit the state-of-the-art machine learning technologies to solve the inverse material design problem.
Despite the rapid development of many MI algorithms and toolboxes, successful discovery of truly
novel materials is still very rare because of the difficulty to access a large data set for any MI applications in order to train a reliable predictive model.
Such difficulty is mainly caused by (1) requirementof high cost and intensive labor, (2) conflict of interest between the industrial companies, and (3) highly
diverse goals. The lack of data problem has become
a bottleneck blocking the advancement of MI.
■ A solution for small data problem— transfer learning
Transfer learning is an indirect alternative to tack-le the lack of data problem. Conceptually, we want to
exploit or generalize knowledge learned from one
task to another, for which human beings are naturally
good at. For example, someone who knows how toplay piano will be able to learn violin or flute fasterthan usual because the knowledge of playing different instruments are expected to be highly correlated. The same idea can be applied to MI in order toeffectively train models with little data. However, we
may not knowledge the correlation between different
material design tasks a priori. To benefit from thetrue power of transfer learning, it is necessary togather a large library of pre-trained models that captures different knowledge in MI.

■ MI software —XenonPy
The Data Science Center for Creative Design and
Manufacturing has developed an all-in-one package,called XenonPy, to provide easy-to-use tools for calculating various material descriptors, as well as machinelearning model training. XenonPy unifies popular descriptors of both organic and inorganic compounds,
including composition descriptor, structural descriptor, classical fingerprints, and neural fingerprints.
Furthermore, the large collection of state-of-the-art
pre-trained models in
XenonPy provides thebest platform for transfer learning in various
MI applications, such as,
small molecules, polymers, crystals, etc.
Hironao Yamada,
Stephen Wu

Figure: Functions of XenonPy.

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Research Center for Medical and Health Data Science

Prediction Intervals for Random-Effects Meta-Analysis■ Meta-analysis
Meta-analysis is an important tool for synthesizing
the results of a set of related studies. A common objective of meta-analysis is to estimate the treatment effectand its confidence interval, and random-effects models
that assume the true treatment effects differ for eachstudy have been widely applied. The average treatment effect across all studies and its confidence interval have been used together with heterogeneity measures that are important for generalizability. Recently, it
is recommended that a prediction interval that can beinterpreted as the range of the predicted true treatment
effect in a new study would be reported alongside a
confidence interval and heterogeneity measure.

■ Problem in conventional prediction intervals
Conventional prediction intervals have a problem of

Figure 1: The random-effects meta-analysis and prediction interval.

severe under-estimation of the range when the numberof studies is small, as is often the case in clinical practice. The reason of the under-estimation has not beenstudied. No explicit solution to this problem has beenfound thus far.

■ The proposed prediction interval
We discussed the reason of the under-estimationand proposed a new prediction interval that is valid
under more general and realistic settings of meta-analyses in medical research. Conventional prediction intervals apply a large sample approximation, but thisapproximation has a detrimental impact on the estimation performance. Therefore, we develop an alternative that is not based on such approximation and showby simulations that its performance is better than conventional prediction intervals. An application to a published random-effects meta-analyses concluded that substantially different result andinterpretation might be obtained from the
proposed and conventional prediction intervals, as shown in Figure 2. Conventional prediction intervals were narrower than the
proposed prediction interval, which meansthat an under-estimation occurred in conventional prediction intervals. We should becautious in using and interpreting conventional approaches. An R package implementing the new method is available (https://cran.
r-project.org/package=pimeta, CRAN Task
View: MetaAnalysis).

Figure 2: An application to a published random-effects meta-analysis that included 22 studies comparing the treatment effect of antidepressantson reducing pain in patients with fibromyalgia syndrome ( Häuser et
al. JAMA 2009; 301: 198–209; Riley et al. BMJ 2011; 342: d549).

10■ Statistical methods for medical andhealth data science
Since results of meta-analyses have beenutilized for formulation of health care policies and medical guidelines, the proposed
method can provide more precise scientific evidence and useful information for clinical practice. This is a collaborative study
with Professor Toshi A. Furukawa, Department of Health Promotion and Human Behavior, Kyoto University Graduate Schoolof Medicine / School of Public Health. We
aim to provide more precise scientific evidence for clinical practice by developing
statistical methods for medical and healthdata science.
Kengo Nagashima

Research Centers

Quantifying Environmental Characteristics in Social Epidemiology— Devising a Method for Estimating “Alleyss”■ Quantifying environmental characteristics
In the field of social epidemiology, physical and mental health problems that often occur in certain groups
are examined in relation to their living environment. In
order to test the hypothesis that particular environmental characteristics affect health, it is first necessary
to accurately quantify these characteristics by devising appropriate indices. In this research, we will devise
and evaluate an index that is useful for probing the relationship between space-structure characteristics(such as the topography, shape of roads, and density ofhouses in each municipality) and suicide incidence.
Previous research has shown that the ability of residents to seek help is significantly higher in areas withrare suicide incidence. One notable space-structure
characteristic common to areas with rare suicide incidence is the high number of alleys in theseareas. As such, it ishypothesized that alleys promote casualconversation and exchange of information between residents that result in
Exist of “alley” istheir individual andestimated on dotted line.
Figure 1: Description of estimating the “alley”.

Alley density(Inverse number)social problems trickling out subconsciously and becoming manifest, which in turn allows for timely external intervention.

■ Proposing a method for estimating alleys
We obtained the assistance of civil and urban engineering researchers and map companies to devise amethod and the parameters required to estimate thenumber of alleys in areas where geospatial data and
GIS (Geographic Information System) suggest the existence of alleys.
According to the Building Standards Act, any roadwider than 4m around a building site must have atleast 2m of it connected to the site itself. This implies
that buildings that do not appear to be connected to aroad in the geospatial data are highly likely to have alley access. In other words, by cross-checking this information obtained from geospatial data with GIS, we
are able to conclude if alleys exist in the immediate vicinity of these buildings of interest (Figure 1).

■ Relationship between alley density andsuicide-SMR
An analysis of the relationship between the alley
density and suicide-SMR (standardized mortality ratio, averaged over 30 years) in each of the 69 municipalities of Mie prefecture
reveals a significant negative correlation. When
we conducted multiple
regression analysis using an interaction term
to represent the strong
correlation between alley
density and coastal regions, the results showedthat even for municipalities in the coastal regions, a higher numberof alleys significantly reduce suicide-SMR (Figure 2).
Mayumi Oka

Suicide SMR

Figure 2: Alley density and Suicide-SMR.

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NOE ( Network Of Excellence ) Project
Building a Framework for Advancing Strategic Research andPursuing a New Approach to Collaborative Research■ Research and Educational Activities asa Biaxial Structure

The Institute of Statistical Mathematics (ISM) pursues
research and education along two lines of basic research,
as well as NOE (Network Of Excellence)-type researchand professional development. Research and education
efforts are conducted by basic research departments
along a horizontal axis, and the NOE-type research centers and the school for professional development are organized along a vertical axis. By its nature, the basic research departments (along the horizontal axis) cuts
across and links various disciplines, with the goal of developing tools for interdisciplinary research. The field ofstatistical mathematics must itself evolve to meet thechanging needs of society and the data environment,
and is therefore constantly evolving as a field of study. At
the same time, there are approaches and directions thathave remained unchanged as the field evolves. For thatreason, we have chosen not to refer to these efforts as“fundamental research” or “foundational research,” butinstead as “basic research”, in order reflect both thefixed and evolving qualities of statistical mathematics.
There are three basic research departments: Statistical
Modeling, Statistical Data Science, and Statistical Inference and Mathematics. All tenured research staff in ISM
are assigned in principle to one of these basic research
departments. These departments engage in cuttingedge research aimed at developing methodologies forrational prediction and decision making, based on dataand existing knowledge. We regularly assess whether
our research system is functioning effectively from the■ NOE ( Network Of Excellence) ProjectIn accordance with the second medium-term plan for
Research Organization of Information and Systems (ROIS,
ISM’s parent organization), ISM had set as a goal the establishment of NOEs (Networks Of Excellence) in statistical mathematics. This FY: 2019-2020 is the 10th year since
the start of the project. Initially, we established NOEs in
Risk Research, Next-Generation Simulation, Survey Science, Statistical Machine Learning, and Service Science.

Period of the First mid-term plan

Period of the Second mid-term plan

Period of the Third mid-term plan

Beginning

Promoting

Developing( 2004-2009 )Forming of
Risk Research Network
Risk Analysis
Research Center
Set: Apr.,2005( 2010-2015 )Risk Research NOE
Risk Analysis
Research Center
Reorganized: 2011

ISM

Next-Generation
Simulation NOE
Research andDevelopment Center
for Data Assimilation

Survey Science
NOE
Survey Science
Center( 2016-2021 )Medical Health Data
Science NOE* including
Risk Research Network

Set: Jan., 2011
Closed: Mar., 2019

* including Medical Health Data

Statistical Machine
Learning NOE
Research Center for
Statistical Machine
Learning

Science Research Network

Research Center for Medical
Health Data Science
Set: Arp., 2018

Set: Jan., 2012

Service Science
NOE
Service Science
Research Center
Set: Jan., 2012
Closed: Feb., 2017

Set: Jan., 2011
Closed: Feb., 2017

ROIS
Figure 1: Brief history of the NOE ( Network Of Excellence ) Project.

12

viewpoints of research trends and prospects in statistical mathematics and we reorganized several groups andtransferred research staff within basic research departments on April 1, 2018.
On the other hand, the NOE-type research centers andthe school for professional development (along the vertical axis) are staffed by permanent researchers within
ISM, project professors/researchers (post-doctoral staff),and visiting professors and researchers. There are fourNOE-type research centers: the Risk Analysis ResearchCenter, the Research Center for Statistical Machine Learning, the Data Science Center for Creative Design and Manufacturing, and the Research Center for Medical Health
Data Science. These centers conduct research activities
that interface statistical mathematics with individual scientific disciplines in order to find solutions to urgent
social problems. And the School of Statistical Thinking
which is for professional development also provides multiple programs aimed at fostering and promoting statistical thinking. Please refer to the page “Project of Fosteringand Promoting Statistical Thinking” for more details.

Data Science forCreative Design and
Manufacturing NOE
Data Science Centerfor Creative Designand Manufacturing

Data Science forEnvironmental
Resource Managing
NOE ( provisional )Promotion Roomin Risk Analysis
Research Center

Set: Jul., 2017

Joint-Support Center for Data Science Research
ROISISM Projects

We reorganized domains of NOEs or NOE-type ResearchCenters, considering the needs of each community as
well as modern society as a whole. We closed the Survey
Science Center, whose projects were transferred to theresearch activities of the Center for Social Data Structuring in the Joint Support Center for Data Science of ROIS.
At the same time, the Service Science Research Center
was also closed, and its projects were distributed according to their methodologies to other NOE-type Research Centers at the end of February 2017. In July 2017,
we launched the Data Science Center for Creative Designand Manufacturing, and initiated the establishment of“Data Science for Creative Design and Manufacturing
NOE”. The Research Center for Medical Health Data Science was set up in April, 2018. The Center, which waslaunched from one of the projects of the Risk Analysis Research Center, is promoting research and educational
activities in Medical Health Data Science domain basedon the knowledge and research networks that ISM has
obtained in statistical mathematics both inside and outside Japan (Figure 1). As of this year, ISM is promoting
the NOE Project in six fields: Risk Research, Next-Generation Simulation, Survey Science, Statistical Machine
Learning, Data Science for Creative Design and Manufacturing, and Medical Health Data Science (Figure 2).

■ Future Perspective of NOE Activities

ernment communities with further opportunities for joint
usage (of facilities) and joint research. ISM continues topromote this NOE Project. As described above, each NOE
research center serves as core hubs in their respective
fields. ISM is promoting the signing of MOUs (Memorandum of Understanding) with research organizations within Japan and overseas, and the number is increasing eachyear, including ones that span multiple research fields.
The goal of ISM is general research in statistical mathematics, which is in demand by various research fields inboth the humanities and science. ISM must respond flexibly to requests from each community and contribute tothem. Reorganization over recent years, based on suchneeds, has aimed at much deeper promotion of the Project.
We held the International External Evaluation last year
and received valuable opinions regarding ISM’s position,
research activities, and presence in an international context, enabling us to examine the appropriateness of ourresearch structure, as well as other issues. ISM is expanding and developing this NOE projects with the aim of establishing new scientific methodologies (“Fourth Paradigm”), creating new research disciplines, and developing
new styles of joint research. For up-to-date informationon the research structures or activities of the Project,please visit the website. We very much appreciate yourcontinued support for this Project.

This NOE Project is a core theme of ISM. To fulfill the
https://www.ism.ac.jp/noe/project/en/
goal of establishing new scientific methodologies in aknowledge-based society, in which
Advice from Advisory Board
Risk Research NOE
Relations in ISM
the importance of knowledge goes
Relations by MOUs
Risk Research Network
beyond merely solving individual
Medical Healthproblems, NOE activities are being
RelatedAdditional related
Data Science
Organizationsorganizations
Next-Generation
systematically pursued under the
NOE
Simulation NOE
Risk Analysis
unified guidelines formulated by
Medical Health Data
Research Center
Science Research
RelatedNetwork
Organizations
the Managing Committee of the
NOE Project. We also commission
Research Center
Data Assimilation
Group, Dept. offor Medical andexperts from the industrial, acaStatistical Modeling
Health Data Science
demic, and government sectors to
Additional related
Additional related
Advisory Board ofManaging Committeeorganizationsorganizations
be members of the Advisory BoardNOE Projectof NOE Projectof the NOE Project, and their adRelated
RelatedNOE Promotion Unit
Organizations
Organizations
vice helps us promote the projectmuch more effectively. The NOE
Survey Science Group,
Data Science Center
Dept. of Statisticalfor Creative Design
Project, which is made possible byand Manufacturing
Data Science
ISM’s special focus in the crossAdditional related
Additional related
Research Centerorganizationsorganizations
disciplinary field of “statisticalfor Statisticalmathematics,” is attracting strong
Machine Learning
Data Science for
Survey Science
Creative Design andsupport from each of these comNOE
RelatedAdditional relatedorganizations
Manufacturing NOE Organizations
munities. On the basis of this projStatistical Machine
ect, ISM, as an Inter-University ReLearning NOE
search Institute, will be providing
the industrial, academic, and govFigure 2: Relationship diagram of the NOE ( Network Of Excellence) Project.

IS M

13

Project for Fostering and Promoting Statistical Thinking

Rapid development of information and communication technology has led to the explosion of data. Now
surrounded by “Big Data”, everybody is expected to “think statistically”. More than ever, there is a need fordata scientists who can handle such big data and are able to extract useful knowledge from it. Meanwhile,
Japanese higher education is exhibiting a deplorable lack of production capacity in terms of data scientists. This can be accounted for by the fact that no academic institution other than ISM has a Ph. D. course
in statistics and the small number of statisticians in academia are isolated from each other, being scatteredover various disciplines. Hoping to gain a little traction on this problem, ISM established the School of Statistical Thinking, into which we integrated all of our educational resources. In FY 2016, ISM established theManaging Committee of School of Statistical Thinking, inviting contributions from outside experts, and in
FY 2017 we launched the Leading DAT program by adopting the suggestions by the committee. The following are the principal projects.

Research Collaboration Startup
The Institute had already been providing a consultation service for statistical science, but alongwith the launch of the School of Statistical Thinking in November 2011, this service was reorganized
as a research collaboration startup. This program,being one of the projects to foster and promotestatistical thinking, is mainly aimed at supporting
applied scientists and other non-experts. Expert
statisticians affiliated with the Institute give them
advice on statistical modeling, data analysis, andresearch. Some cases have developed into official
research collaborations, which are our primary
duty as an inter-university research institute. The

Institute accepts more than 30 cases annually, someof which benefit society in diverse ways.

Open-type Professional Development ProgramThis is a spin-out program from ISM cooperative
research projects. Establishing a goal is an indispensable element of the proposal of a cooperative
research project. On the other hand, such goal
setting is irrelevant for a summer school program,study session, or retreat. Since the launch of the
School of Statistical Thinking, organizers of suchgroup-oriented study programs can apply to theOpen-type Professional Development Program.
There are two categories under this program: oneis ‘workshop’ and the other is ‘intensive training foryoung researchers’. For FY 2018, five workshops
and one intensive training for young researchers
have been accepted after review.

Open Lecture
We hold an open lecture to introduce the Insti­
tute’s activities and to promote statistical science.
We invite lecturers to speak on a timely topic relat­
ing to statistical science. The lecture is open to the14

general public. For further information, please visit
the website of the Institute of Statistical Mathe­mat­ics.
https://www.ism.ac.jp/kouenkai/

ISM Projects

Statistical Mathematics Seminar Series
The Institute holds weekly seminar series on
statistical mathematics every Wednesday. The
seminars are led by in-house and external lecturers to showcase their latest studies. These lectures

are free to attend. To view the seminar scheduleand learn more about the program, please visit
the Institute of Statistical Mathematics website.
https://www.ism.ac.jp/index_e.html

Data Science Research Plaza
Researchers funded by private-sector firms canmaintain a desk and phone in the School of Statistical Thinking. This program is subject to fees,
and the contract can be renewed annually. A faculty mentor gives advice to the accepted funded

researcher so that he or she can freely attend various events, such as seminars, workshops, conferences, and extension courses. After learning theexpertise of the ISM research staff, participants inthis program are invited to take advantage of paid
consultations and funded research collaboration.

Leading DATIn FY 2017, the School of Statistical Thinking
launched a program called “Leading DAT” aimed
at training data scientists with the knowledge andskills in statistical mathematics required by modern society. In FY 2018, we organized four Leading
DAT lectures entitled “L-A Introductory Data Science”, “L-B1 An Introduction to Statistical Modeling”, “L-B2 Machine Learning and Modern Methodologies in Data Science” and “L-S Geographic
Information and Spatial Modeling”. Around one
third of the attendants of L-B1 and L-B2 enroll the
Leading DAT Training Course, in which we grantcertificates to participants who have fulfilled thecourse requirements, including attendance in L-B1

and L-B2 lectures and submission of reports. A
total of 27 people have been granted the certificate of completion.

Certificate ceremony

Tutorial Courses
The education program at ISM dates back to
1944, the year of founding. The Ministry of Education installed a training center within ISM to foster
technicians in numerical computation. After theWorld War II, this training center was relaunched
in 1947 to develop pollsters and census takers. It
helped to cultivate professionals in the field of statistical surveys, while a growing number of entries
from business and industry coming for various
types of training were also observed.
Now the tutorial courses are operated by the

School of Statistical Thinking, which was established in 2011.
In FY 2018, 10 lectures and one course (including Leading DAT) were held and the number of participants was 993. The total numbers of lecturesand courses held from 1969 to March, 2019 were
373 and 2 respectively, with a total of 27,021 participants. These lectures covered a wide range offields from basic to applied statistics. The scheduleof tutorial courses can be found on the website of
the Institute of Statistical Mathe­matics.
https://www.ism.ac.jp/lectures/kouza.html
15

Research Cooperation

International Cooperation■ Associated Foreign Research Institutes
Organization name

Address

Conclusion day

The Statistical Research Division of the U.S. Bureau of the Census

USA ( Washington )July 27, 1988

Stichting Mathematisch Centrum

The Kingdom of the Netherlands( Amsterdam )May 10, 1989

Institute for Statistics and Econometrics, Humboldt University of Berlin

Germany ( Berlin )December 8, 2004

The Steklov Mathematical InstituteRussia ( Moscow )August 9, 2005

Central South University

China ( Changsha )November 18, 2005

Soongsil University

The Republic of Korea ( Seoul )April 27, 2006

University of Warwick

The United Kingdom ( Coventry )January 16, 2007

Indian Statistical Institute

India ( Kolkata )October 11, 2007

Institute of Statistical Science, Academia Sinica

Taiwan ( Taipei )June 19, 2008

Department of Empirical Inference,
Max Planck Institute for Biological Cybernetics

Germany ( Tubingen )August 11, 2010

Department of Communication Systems,
SINTEF Information and Communication Technology

Norway ( Trondheim )January 30, 2012

University College London

The United Kingdom ( London )February 16, 2012

Department of Electronics and Telecommunications,
Norwegian University of Science and Technology

Norway ( Trondheim )May 22, 2012

Department of Probability and Mathematical Statistics,
Charles University in Prague

Czech Republic (Prague )October 10, 2012

Department of Ecoinformatics,
Biometrics and Forest Growth of the Georg-August University of Goettingen

Germany (Goettingen )October 18, 2012

Korean Statistical Society (KSS )The Republic of Korea ( Seoul )July 9, 2013

Toyota Technological Institute at Chicago

USA ( Chicago )February 10, 2014

Australian National University

Australia ( Canberra )May 15, 2014

RiskLab ETH Zurich

Switzerland ( Zurich )February 7, 2015

Institut de Recherche en Composants logiciel et matériel pour l’Information
et la Communication Avancee

France ( Paris )February 9, 2015

Le laboratoire de mathématiques de I’Universite Blaise Pascal

France (Clermont-Ferrand )February 11, 2015

Centre de Rechereche en Informatique, Signal et Automatique de Lille

France ( Paris )February 12, 2015

University College London Big Data Institute

The United Kingdom ( London )February 26, 2015

The Institute of Forestry, Pokhara of Tribhuvan University

Nepal ( Pokhara )March 6, 2015

The Institute of Forest and Wildlife Research and Developmentof the Forestry Administration of Cambodia

Cambodia ( Phnom Penh )March 6, 2015

The Chancellor masters and Scholars of the University of Oxford

The United Kingdom ( Oxford )March 10, 2015

Forest Inventory and Planning Institute of Vietnam

Vietnam ( Hanoi )June 2, 2015

Zuse Institute Berlin

Germany (Berlin )June 20, 2016

The University of Porto

Portugal ( Porto )June 22, 2016

Natinonal University of Laos

Laos ( Vientiane )March 15, 2017

Institute of Geophysics China Earthquake Administration

China ( Beijing )April 28, 2017

Hong Kong Baptist University

Hong Kong ( Kowloon Tong )August 28, 2017

University of Malaya

Malaysia ( Kuala Lumpur)

September 18, 2017

Unversidade de Évola

Portugal ( Evola )November 30, 207

Universität Ulm

Germany ( Ulm )December 8, 2017

The Korean Association for Survey Research

The Republic of Korea ( Seoul )February 14, 2018

The Jean Golding Institute for data-intensive research, University of Bristol

The United Kingdom ( Bristol)January 15, 2019

Survey Research Center, Sungkyunkwan University

The Republic of Korea ( Seoul )February 25, 2019

University of LampungIndonesia ( Lampung )March 6, 2019

Department of Earth and Space Sciences,
Southern University of Science and Technology

China ( Shenzhen Shi )March 25, 2019

Université Bretagne Sud

France ( Lorient )March 29, 2019

* There are two more agreements concluded.

16

Research Collaboration
ISM performs many activities for collaborating with researchers in the various fields of statistical science,from the individual level to the national level. The ISM cooperative research program regularly performs research activities to provide the research resources of ISM to researchers at universities or research institutes inorder to advance their academic research. Available research resources include books, journals, supercomputers, some commercial statistical software packages, as well as statistical packages developed by ISM, and also
the researchers in ISM themselves, who have abundant professional knowledge and experience in statisticalscience and data analysis. The ISM cooperative research program provides not only research support funds
but also opportunities for the various researchers in many fields who require statistical knowledge to make useof the resources available at ISM. ISM’s aim is to be a place for interaction and fusion among researchers insideand outside of ISM, and to contribute to multidisciplinary development of both the theory and the applicationof statistical science.

■ Number of Activities
Year

2013

2014

2015

2016

2017

2018

Number of Activities

181

177

183

187

161

166

■ Fields of Research Collaboration
Research collaboration is classified by research field as follows. Applicants can use the table below to findthe most appropriate type of project.
ISM Fields
NumberFieldsNumberFieldsa

Prediction and Control Groupf

Structure Exploration Groupb

Complex System Modeling Groupg

Mathematical Statistics Groupc

Data Assimilation Grouph

Learning and Inference Groupd

Survey Science Groupi

Mathematical Optimization Groupe

Metric Science Groupj

OthersMajor Research Fields
NumberFieldsMajor Research Domains

1

Statistical mathematics

Mathematical theory of statistics, optimization, etc.

2Information science

Algorithms, use of computer in statistics, etc.

3

Biological science

Medicine, pharmacy, epidemiology, genetics, etc.

4Physical science

Space, planet, earth, polar region, materials, etc.

5Engineering

Mechanics, electronics, control, chemistry, architecture, etc.

6

Human science

Philosophy, art, psychology, education, history, geography, culture, language, etc.

7Social science

Economics, law, politics, society, manage­ment, official statistics, population, etc.

8

Environmental science

Environmental statistics, environmentrics, agricultural statistics, statistical meteorology,
land economics, landscape management, forest management, etc.

9

OthersOther research fields

17Graduate School Program

Organization
The Institute of Statistical Mathematics is one of the platforms of SOKENDAI (The Graduate University for
Advanced Studies; the headquarters in Hayama, Kanagawa), which was opened in October 1988 to offer graduate education. Since its opening, the Institute has included the Department of Statistical Science and, sinceApril 1989, has accepted students for education and research in doctoral programs. In 2006, the Instituteadopted a five-year system, offering either a five-year education and research program, or a three-year education and research program starting from the third year of study.
Regional Studies
Comparative Studies

School of Cultural and Social Studies

Japanese Studies
Japanese History
Japanese Literature
Structural Molecular Science
Functional Molecular Science

School of Physical Sciences

Astronomical Science
Fusion Science
Space and Astronautical Science

SOKENDAI

Accelerator Science

School of High Energy Accelerator Science

Materials Structure Science
Particle and Nuclear Physics
Statistical Science

School of Multidisciplinary Sciences

Polar Science
Informatics
Genetics

School of Life Science

Basic Biology
Physiological Sciences

School of Advanced Sciences

Evolutionary Studies of Biosystems

Outline of Education and Research
The Department of Statistical Science, which is based on the Institute of Statistical Mathematics (ISM) serving
as its underlying platform, aims to cultivate individuals who possess creative research skills to contribute tosolving various important intricately-intertwined problems. To this end, the Department conducts education
and research related to the basis, mathematics and applications of data collection designs, modeling, inference
and prediction, and equip students with the ability to extract information and knowledge from the real world
based on the effective use of data.
Field of Education and Research

18

Contents

Statistical Modeling

Education and research focuses on dynamic modeling such as spatial and space-time modeling, graphicalmodeling of temporally and/or spatially inter-related complex phenomena, and intelligent information
processing. We also provide education and research on statistical inference based on various models,
methods of calculation for inference, and evaluation of models based on data.

Statistical Data Science

We provide education and research on data design, investigation and analytical methods to cope with theuncertainty and incompleteness of information, as well as on computational statistics.

Statistical Inference
and Mathematics

We provide education and research on the theory of statistics and related basic mathematics, statistical learningtheory to extract information from data by automated learning and inference techniques; as well as theory andapplications of optimization and computation algorithms which serve as the basis for computational inference.

Features of Education and Research● The course is the only integrated doctoral program on statistical science in Japan. It has received students

from a wide variety of disciplines and has offered education and research on statistical science by professors specialized in many different fields, from theory through to practical applications.

● The Institute of Statistical Mathematics, the platform for the course, is equipped with a world-class supercomputer, high-speed 3D graphic computers and simulators to generate physical random numbers, as well
as a variety of software, including original statistical software developed by the Institute.

● The academic publications and books on statistical and mathematical sciences produced are some of thebest in the world.

● In its role as an inter-university research institute, the Institute holds frequent workshops and seminars byvisiting professors and researchers from both Japan and abroad. Students are free to attend and participate.

● It is possible to collaborate with researchers from other universities and institutions. It is also possible forstudents to develop their own projects by participating in research projects with other institutions.

Course Requirements and Type of Degree Granted● Requirements to complete the doctoral course are as follows:

Completion of at least 40 credits, including the required ones, by a student in the five-year program, or completion of at least 10 credits by a three-year doctorate student who previously completed a Master’s course;
meeting all the criteria set by the thesis committee of the Institute; and successfully completing the examination.

● On completion of the course, either a Doctorate in Statistical Science or, if the thesis deals mainly with aninter-disciplinary field related to statistical science, a Doctorate of Philosophy is awarded.

● The required number of years of study will be flexible if a student demonstrates outstanding research results.

Number of Students ( As of April 1, 2019 )■ 5-year doctoral

course:Quota,2■ 3-year doctoral

course:Quota,3Year of enrollment

2010

2013

2016

2017

2018

2019

Number of students

1

1

1

2

1

3

Year of enrollment

2014

2015

2016

2017

2018

2019

Number of students

1①

1①

4③

4③

7⑤

9⑦

* The figures in circles indicate those who are employed by other organizations.

University Background of Students
National and public universities• Hokkaido University ( 5 ) • Tohoku University ( 3 ) • Fukushima University (1) • University of Tsukuba (6 ) • Saitama University (1)• Chiba University (1) • Ochanomizu University (1) • National Graduate Institute for Policy Studies (1) • Tokyo Medical and Dental
University (1) • Tokyo University of Marine Science and Technology (1) • Tokyo Gakugei University ( 2 ) • Tokyo Institute ofTechnology ( 6 ) • The University of Tokyo (20 ) • Tokyo Metropolitan University (1) • Tokyo University of Agriculture and Technology (1)• Hitotsubashi University (6 ) • Shizuoka University (1) • Japan Advanced Institute of Science and Technology (1) • Nagoya University (3 )• Toyohashi University of Technology ( 2 ) • Kyoto University ( 7 ) • Osaka City University (1) • Osaka University ( 3 ) • Nara Institute of
Science and Technology (1) • Okayama University ( 2 ) • Shimane University (3 ) • Kyushu University ( 3 ) • Oita University (1)19Graduate School Program

University Background of Students
Private universities• Kitasato University (1) • Keio University (8 ) • International Christian University (1) • Shibaura Institute of Technology (1) • Chuo
University (9 ) • Tokyo University of Science (7 ) • Toyo University (1) • Japan Women’s University (1) • Nihon University (2 ) • Hosei
University (7 ) • Waseda University (9 ) • Nanzan University (1) • Osaka Electro-Communication University ( 1 ) • Kansai University ( 1 )• Kyoto Sangyo University (1) • Ritsumeikan University (1) • Okayama University of Science (1) • Kurume University (1)Foreign universities• Aston University (1) • University of California, Irvine (1) • California State University, Long Beach (1) • University of Campinas (1)• University of Colorado Boulder (2) • University of Dhaka (2) • University of Hawaii (1) • Jahangirnagar University (2)• University of Malaya (1) • Northeast Normal University (1) • Ohio University (2) • University of Rajshahi (2) • Stanford University (1)• The University of Nottingham (1) • Institute of Applied Mathematics, AMSS, CAS (1) • University of Science and Technology ofChina (1) • Center for Analysis and Prediction, China Seismological Bureau (1) • Northeastern University (1) • The Hong Kong
University of Science and Technology (1)Degrees AwardedYear
Doctor of Philosophy

2013

2014

2015

2016

2017

2018

6

5

5

7

5

5

AlumniNational and public universities, and public organizations• Obihiro University of Agriculture and Veterinary Medicine • University of Tsukuba • University of Hyogo • The University of Tokyo• The University of Electro-Communications • Saitama University • Nagoya University • Kyushu University • Kyushu Institute ofTechnology • University of the Ryukyus • The Institute of Statistical Mathematics • Tohoku University • Yokohama National University• Hokkaido University • Tokyo Institute of Technology • Hiroshima University • Oita University of Nursing and Health Sciences• JAXA’s Engineering Digital Innovation Center • Kyoto University • Nara Institute of Science and Technology • Bank of Japan• Japan Broadcasting Corporation • Railway Technical Research Institute • Statistical Information Institute for Consulting and Analysis• Government Pension Investment Fund • Public School • RIKEN • Statistics Bureau of Japan
Private universities• Sapporo Gakuin University • Tokyo Health Care University • Meiji University • Doshisha University • Josai University • Nihon
University • Komazawa University • Aichi University of Technology • Tokyo University of Information Sciences • Shibaura Instituteof Technology • Rikkyo University • Waseda University • Keio University
Foreign universities• Jahangirnagar University • Victoria University • Massey University • University of Otaga • Statistics New Zealand • University of
Rajshahi • University of California, Los Angeles • Asia-Pacific Center for Security Studies Department • Central South University• Hong Kong Baptist University • University of South Carolina • The University of Warwick
Private companies, etc.
• Hitachi, Ltd. Central Research Laboratory • NTT Communication Science Laboratories • Seiwa Kikaku • NLI Research Institute• Mizuho Trust and Banking • Nomura Securities Co., Ltd. • ATR Computational Neuroscience Laboratories • Toyota Motor
Corporation, Higashi-Fuji Technical Center • Schlumberger Limited • Macquarie Securities, Japan • Non-Life Insurance Rating
Organization of Japan • Barclays Global Investors • Open Technologies Corporation • Yamaha Corporation • Goldman Sachs
Asset Management L.P. • CLC bio Japan, Inc. • Bank of Tokyo-Mitsubishi UFJ • Pfizer Japan Inc. • Doctoral Institute for Evidence
Based Policy • Sony Corporation • NTTIT Corporation • Sompo Japan Insurance Inc. • Qualicaps Co.,Ltd. • Bridgestone
Corporation • Brain Pad Inc. • Sumitomo Chemical Co.,Ltd. • PricewaterhouseCoopers Aarata • Mitsubishi Tanabe Pharma
Corporation • Daiichi Sankyo Co.,Ltd. • Shizuoka Cancer Center • CPC Clinical Trial Hospital, Medipolis Medical Research Institute• CRD Association • Japan Society for the Promotion of Science • Tokyo Electric Power Company Holdings, Inc. • Asahi Kasei
Corporation • Honda R&D Co.,Ltd. • Yokogawa Electric Corporation • Kao Corporation • Advanced Smart Mobility Co., Ltd.

20

Facilities and Equipment

Computational Resources ( As of April 1, 2019 )ISM is now operating two different supercomputer
systems. One is the Supercomputer System for Statistical
Science, which was introduced in FY2018. The other system is Communal Cloud Computing System (called System “C”) introduced at the end of FY2013.
The Supercomputer System for Statistical Science, an
HPE SGI 8600 system, has been operated since October
2018. The system is a distributed-memory parallel computer that has total theoretical peak performance of 1.49
petaflops. The system is liquid cooled and consists of
384 compute nodes. Each node has two CPU chips (Intel
Xeon GOLD 6154) with 18 cores and has 384 GB memory.
The system includes hardware random number generator. For visual representation of research results, ISM
has a 200-inch wide screen and a projector capable ofshowing 3D movies in 4K resolution in the historicalcomputers exhibit room.
System “C” consists of 64 Dell PowerEdge R620 (two10-core Xeon E5-2680v2, 256GB memory). This systemprovides easy-to-use computing environments such as
distributed-memory statistical computing environments
and Web servers, running on Apache CloudStack software. This private cloud system is also used in hostingdata analysis competition events.
In the main office building, the primary local area
network (LAN) consists of an Ethernet network using
10GBase-SR for the main trunk and 1000Base-T forbranches. The personal computers in researchers’ offices, and the two supercomputer systems are all connected to this network. A wireless LAN system is also available in the immediate area of the building occupied by
ISM. These LAN systems enable distributed processing
and allow computing resources and statistical data to
be used effectively. Comprehensive network security
methods have been implemented, such as a firewall
system, anti-virus software, and an intrusion prevention system. To encourage joint research with researchers both in Japan and abroad, as well as the exchangeof e-mails, the network is connected to the Internet
through SINET5 (40 Gbps).

Supercomputer System for Statistical Science ( HPE SGI 8600 )

200-inch wide screen showing 3D movies in 4K resolution

Library and Materials ( As of April 1, 2019 )We have a large number of major Japanese/foreign
journals covering a wide variety of fields including statistics, mathematics, computer science and informatics. In
addition, we also have a large library consisting of books
on humanities, social science, biology, medical science,science and engineering.
Besides contributed to Japanese and foreign pub­lications, we also have a collection of journals that wepublish ourselves: Annals of the Institute of Sta­tistical
Mathematics (English; Springer), Proceedings of the Institute of Statistical Mathematics (Japanese), ISM Survey
Research Report (Statistical Researches mainly related
to the Japanese National Character), Computer Science
Monographs, Cooperative Research Re­ports (for collaborative research projects), Research Memorandum, ISM

Reports on Statistical Computing, and ISM Report on
Research and Education.
All materials are properly catalogued and can besearched from the web in order to meet the needs ofresearch­ers working in a wide of fields. We also accept
photocopy requests.
Japanesebooks
19,99928%

Books
72,381

Japanese
periodicals
1,184
53%

Foreignperiodicals

Periodicals
2,218

1,034
47%

Foreign books
52,38272%

21

Finance and Buildings

Administration Subsidy and Others ( 2018)
Type

Personnel expenses

Non-personnel expenses

Total

665,753

953,057

1,618,810

Expenditure

Unit: 1,000JPY

Accepted External Funds ( 2018)
Type

Joint
research

Subcontracted
research,
Trustee businessAcademic
Consulting

Contract
researchers

Contribution for
scholarship

Total

Items

23

24

10

3

7

67

Income54,913

137,514

10,680

1,672

8,777

213,556
Unit: 1,000JPY

Grant-in-Aid for Scientific Research “ KAKENHI” ( 2018)
Research Category

Items

Amount Granted

Grant-in-Aid for Scientific Research on Innovation Areas——

Grant-in-Aid for Scientific Research ( S )——Grant-in-Aid for Scientific Research ( A)5

63,180Grant-in-Aid for Scientific Research ( B)933,670Grant-in-Aid for Scientific Research ( C)20

19,711

Grant-in-Aid for Challenging Research ( Exploratory )

3

6,370

Grant-in-Aid for Young Scientists ( B )1112,306

Grant-in-Aid for Early-Career Scientists

5

4,179

Grant-in-Aid for Research Activity Start-up

1

1,430

Grant-in-Aid for JSPS Fellows

1

1,690

55

142,536

Total

Unit: 1,000JPY

Site and Buildings ( As of April 1, 2019)22

Site Area

62,450m 2

Area for Buildings (total)

16,209m 2

Organization

Organization Diagram ( As of April 1, 2019 )Research Ethics Review Committee

Managing Committee of School of
Statistical Thinking

Cooperative Research Committee

Council

Advisory Board

Department of Statistical
Modeling
Department of Statistical
Data Science
Department of Statistical
Inference and Mathematics■ Prediction and Control Group■ Complex System Modeling Group■ Data Assimilation Group■ Survey Science Group■ Metric Science Group■ Structure Exploration Group■ Mathematical Statistics Group■ Learning and Inference Group■ Mathematical Optimization Group

Risk Analysis Research Center

Director-General

Research Center for
Statistical Machine Learning
Data Science Center for Creative
Design and Manufacturing

Vice Director-General

Research Center for Medical and
Health Data Science
School of Statistical Thinking
Center for Engineering andTechnical Support
Library

Planning Unit

■ Computing Facilities Unit
■ Computer Networking Unit
■ Information Resources Unit
■ Media Development Unit

■ URA Station

Evaluation Unit
Information and Public Relations Unit
Administration Planningand Coordination Section

Industry-Academia Collaboration and Intellectual Property Unit
NOE Promotion Unit
Gender Equality Unit
International Affairs Unit

General Affairs Division
Tachikawa Administration
Department

Financial Division
Accounting Division
Research Promotion Division23

Number of Staff ( As of April 1, 2019 )DirectorGeneral

Type

Director-General

Assistant Administ­ Technical
Professor Associate
Staff
Professor Professor rative Staff

Total

1

1

Department of Statistical Modeling

6

7

2

15

Department of Statistical Data Science

5

6

3

14

Department of Statistical Inference and Mathematics

7

5

3

15

School of Statistical Thinking

0Center for Engineering and Technical Support

11

Administration Planning and Coordination Section
Tachikawa Administration Department
Total

1

18

18
(

8

11

1

1

( 33)

( 33)

1 ( 33)1157 ( 33 )) Total number of staff of Tachikawa Administration Department.

The number under Technical Staff at the Center for Engineering and Technical Support and Administrative Staff at the Tachikawa Administration Department
include two each staff member who retired because of age but was reemployed in a different position.

Staff ( As of August 1, 2019 )Director-General

Hiroe TSUBAKI

Vice Director-General

Satoshi ITO

Vice Director-General

Satoshi YAMASHITA

Vice Director-General

Yoshihiko MIYASATO

Department of Statistical Modeling
Director

Tomoko MATSUI• Prediction and Control Group ————————————————————————————————————————————————————————————
Prof.

Yoshinori KAWASAKIProf.

Yoshihiko MIYASATO

Prof.

Atsushi YOSHIMOTO

Assoc. Prof.

Jiancang ZHUANG

Assoc. Prof.

Yumi TAKIZAWA

Assoc. Prof.

Fumikazu MIWAKEICHI• Complex System Modeling Group ——————————————————————————————————————————————————————————Prof.

Tomoko MATSUIProf.

Yukito IBA

Assoc. Prof.

Kazuhiro MINAMI

Assoc. Prof.

Shinsuke KOYAMA

Assoc. Prof.

Hideitsu HINO

Assist. Prof.

Momoko HAYAMIZU• Data Assimilation Group ———————————————————————————————————————————————————————————————Prof.

Genta UENO

Assoc. Prof.

Shinya NAKANOAssist. Prof.

Shunichi NOMURA

Project Assist. Prof. Takashi YAMAMOTO

Visiting Prof.

Shinichi OTANIVisiting Prof.

Visiting Prof.

Masako KAMIYAMA

Visiting Assoc. Prof. Yosuke FUJII

Kazuyuki NAKAMURA

Project Assoc. Prof. Masaya SAITO
Visiting Prof.

Tadahiko SATO

Visiting Prof.

Tomoyuki HIGUCHI

Visiting Assoc. Prof. Hiromichi NAGAO

Visiting Assoc. Prof. Hiroshi KATO

Department of Statistical Data Science
Director

Koji KANEFUJI• Survey Science Group —————————————————————————————————————————————————————————————————————
Prof.

Ryozo YOSHINO

Project Assist. Prof. Yusuke INAGAKI

24

Assoc. Prof.

Tadahiko MAEDA

Project Assist. Prof. Kiyohisa SHIBAIAssoc. Prof.

Yoo Sung PARK

Project Assist. Prof. Le Duc ANH

Organization

Staff
Department of Statistical Data Science
Project Researcher

Naoko KATO

Visiting Prof.

Toru KIKKAWA

Visiting Prof.

Yoshimichi SATO

Visiting Prof.

Wataru MATSUMOTO

Visiting Prof.

Kazufumi MANABE

Visiting Prof.

Takatoshi IMADA

Visiting Prof.

Masahiro MIZUTA

Visiting Assoc. Prof. Taisuke FUJITA

Visiting Assoc. Prof. Koken OZAKI• Metric Science Group —————————————————————————————————————————————————————————————————
Prof.

Satoshi YAMASHITA

Prof.

Koji KANEFUJI

Prof.

Yoichi ITO

Assoc. Prof.

Ikuko FUNATOGAWA

Assoc. Prof.

Hisashi NOMA

Assist. Prof.

Nobuo SHIMIZU

Project Researcher

Hiroka HAMADA• Structure Exploration Group —————————————————————————————————————————————————————————————Prof.

Ryo YOSHIDA

Assoc. Prof.

Jun ADACHIAssist. Prof.

Stephen WU

Assist. Prof.

Daisuke MURAKAMI

Assoc. Prof.

Kenichiro SHIMATANI

Department of Statistical Inference and Mathematics
Director

Satoshi KURIKI• Mathematical Statistics Group ———————————————————————————————————————————————————————————————
Prof.

Satoshi KURIKI

Prof.

Yoshiyuki NINOMIYAAssoc. Prof.

Shuhei MANO

Assoc. Prof.

Shogo KATO

Assoc. Prof.

Takaaki SHIMURA

Visiting Prof.

Akimichi TAKEMURA• Learning and Inference Group —————————————————————————————————————————————————————————————
Prof.

Shinto EGUCHIProf.

Kenji FUKUMIZU

Prof.

Hironori FUJISAWA

Assoc. Prof.

Daichi MOCHIHASHI

Assoc. Prof.

Masayuki HENMI

Assist. Prof.

Ayaka SAKATA

Assist. Prof.

Masaaki IMAIZUMI• Mathematical Optimization Group —————————————————————————————————————————————————————————
Prof.

Satoshi ITO

Visiting Prof.

Eitarou AIYOSHIProf.

Shiro IKEDA

Assist. Prof.

Mirai TANAKA

Risk Analysis Research Center
Director

Satoshi YAMASHITA

Vice Director

Shogo KATO

Prof.

Satoshi YAMASHITA

Prof.

Satoshi KURIKI

Prof.

Koji KANEFUJI

Prof.

Tomoko MATSUIProf.

Atsushi YOSHIMOTO

Prof.

Yoshinori KAWASAKIProf.

Yoshiyuki NINOMIYAAssoc. Prof.

Masayuki HENMI

Assoc. Prof.

Jiancang ZHUANG

Assoc. Prof.

Kenichiro SHIMATANI

Assoc. Prof.

Shogo KATO

Assoc. Prof.

Yumi TAKIZAWA

Assoc. Prof.

Shuhei MANO

Assoc. Prof.

Kazuhiro MINAMI

Assoc. Prof.

Takaaki SHIMURA

Assist. Prof.

Shunichi NOMURA

Assist. Prof.

Stephen WU

Assist. Prof.

Daisuke MURAKAMI

Project Assist. Prof. Yicun GUO

Project Assist. Prof. Junchao ZHANG

Project Assist. Prof. Yuma UEHARA

Project Assist. Prof. Hideaki NAGAHATA

Visiting Prof.

Yo SHIINA

Visiting Prof.

Hisayuki HARA

Visiting Prof.

Toshinao YOSHIBA

Visiting Prof.

Nakahiro YOSHIDA

Visiting Prof.

Rinya TAKAHASHI

Visiting Prof.

Toshikazu KITANO

Visiting Prof.

Sadaaki MIYAMOTO

Visiting Prof.

Shigeyuki MATSUIVisiting Prof.

Takahiro HOSHINO

Visiting Prof.

Shinsuke ITO

Visiting Prof.

Hitoshi MOTOYAMA

Visiting Prof.

Takashi KAMEYA

Visiting Prof.

Shunji HASHIMOTO

Visiting Prof.

Naoki SAKAI

Visiting Prof.

Satoshi TAKIZAWA

Visiting Prof.

Mihoko MINAMI

Visiting Prof.

Toshihiro HORIGUTI

25

Risk Analysis Research Center
Visiting Prof.

Naoto KUNITOMO

Visiting Prof.

Hideatsu TSUKAHARA

Visiting Prof.

Toshio HONDA

Visiting Prof.

Masaaki FUKASAWA

Visiting Prof.

Yasutaka SHIMIZU

Visiting Prof.

Takaaki YOSHINO

Visiting Prof.

Michiko MIYAMOTO

Visiting Prof.

Tadashi ONO

Visiting Prof.

Hiroshi TSUDA

Visiting Prof.

Satoshi FUJII

Visiting Prof.

Masakazu ANDO

Visiting Assoc. Prof. Masao UEKI

Visiting Assoc. Prof. Takafumi KUBOTA

Visiting Assoc. Prof. Yukihiko OKADA

Visiting Assoc. Prof. Junichi TAKAHASHI

Visiting Assoc. Prof. Seisho SATO

Visiting Assoc. Prof. Yuta KOIKE

Visiting Assoc. Prof. Kenichi KAMO

Visiting Assoc. Prof. Masashi KONOSHIMA

Visiting Assoc. Prof. Tetsuji TONDA

Visiting Assoc. Prof. Bogdan Dumitru ENESCU

Visiting Assoc. Prof. Kazuyoshi NANJO

Visiting Assoc. Prof. Takaki IWATA

Visiting Assoc. Prof. Teppei OGIHARA

Visiting Assoc. Prof. Dou XIAOLING

Research Center for Statistical Machine Learning
Director

Kenji FUKUMIZU

Vice Director

Tomoko MATSUIProf.

Kenji FUKUMIZU

Prof.

Tomoko MATSUIProf.

Shinto EGUCHIProf.

Yoshihiko MIYASATO

Prof.

Satoshi ITOProf.

Shiro IKEDA

Prof.

Satoshi KURIKI

Prof.

Hironori FUJISAWA

Assoc. Prof.

Daichi MOCHIHASHI

Assoc. Prof.

Shinsuke KOYAMA

Assoc. Prof.

Kazuhiro MINAMI

Assoc. Prof.

Hideitsu HINO

Assist. Prof.

Mirai TANAKA

Assist. Prof.

Daisuke MURAKAMI

Assist. Prof.

Masaaki IMAIZUMIProject Assist. Prof. Mikio MORII

Project Assoc. Prof. Shinichiro GOTO
Assist. Prof.

Ayaka SAKATA

Project Assist. Prof. Sho SAITO

Project Assist. Prof. Yoichi MOTOTAKE

Project Assist. Prof. Ye CHEN

Project Researcher

Takuo HAMAGUCHI

Visiting Prof.

Yoichi MOTOMURA

Visiting Prof.

Nobuhiko TERUIVisiting Prof.

Yoshiki YAMAGATA

Visiting Prof.

Masataka GOTO

Visiting Prof.

Arthur GRETTON

Visiting Prof.

Takashi TSUCHIYA

Visiting Prof.

Akiko TAKEDA

Visiting Prof.

Katsuki FUJISAWA

Visiting Prof.

Yuji SHINANO

Visiting Assoc. Prof. João Pedro PEDROSO

Visiting Assoc. Prof. Tsutomu TAKEUCHI

Visiting Assoc. Prof. Tsukasa ISHIGAKI

Visiting Assoc. Prof. Makoto YAMADA

Visiting Assoc. Prof. Eiji MOTOHASHI

Data Science Center for Creative Design and Manufacturing
Director

Ryo YOSHIDA

Vice Director

Hironori FUJISAWAProf.

Ryo YOSHIDA

Prof.

Hironori FUJISAWA

Prof.

Kenji FUKUMIZU

Assoc. Prof.

Shinya NAKANOAssoc. Prof.

Daichi MOCHIHASHI

Assist. Prof.

Stephen WU

Project Researcher

Yo NOGUCHI

Visiting Assoc. Prof. Terumasa TOKUNAGA

Project Assist. Prof. Liu CHANG

Research Center for Medical and Health Data Science
Director

Vice Director

Hisashi NOMAProf.

Yoichi ITOProf.

Satoshi YAMASHITA

Prof.

Shinto EGUCHI

Assoc. Prof.

Hisashi NOMA

Assoc. Prof.

Masayuki HENMI

Assoc. Prof.

Ikuko FUNATOGAWA

Assoc. Prof.

Fumikazu MIWAKEICHI

Project Assoc. Prof. Kengo NAGASHIMA

Project Assist. Prof. Naomi TAMURA

26

Yoichi ITO

Project Assist. Prof. Mayumi OKA

Project Researcher

Yasuyuki HAMURA

Visiting Prof.

Yasuo OHASHI

Visiting Prof.

Senichiro KIKUCHI

Visiting Prof.

Ken KIYONO

Visiting Prof.

Tatsuhiko TSUNODA

Visiting Prof.

Hisateru TACHIMORI

Visiting Prof.

Satoshi TERAMUKAI

Visiting Prof.

Toshiya SATO

Visiting Prof.

Satoshi HATTORI

Visiting Prof.

Manabu IWASAKI

Visiting Prof.

Shigeyuki MATSUIVisiting Prof.

Michiko WATANABE

Visiting Assoc. Prof. Ryoichi KIMURA

Visiting Assoc. Prof. Masataka TAGURIOrganization

Staff
Research Center for Medical and Health Data Science
Visiting Assoc. Prof. Yasunori SATO

Visiting Assoc. Prof. Ryota NAKAMURA

Visiting Assoc. Prof. Atsushi GOTO

Visiting Assoc. Prof. Kunihiko TAKAHASHI

Visiting Assoc. Prof. Kazushi MARUO

School of Statistical Thinking
Director

Yoshinori KAWASAKI

Vice Director

Yukito IBA

Prof.

Yoshinori KAWASAKIProf.

Satoshi ITO

Prof.

Yukito IBA

Prof.

Kenji FUKUMIZU

Prof.

Yoshiyuki NINOMIYA

Project Prof.

Yoshiyasu TAMURA

Assoc. Prof.

Kenichiro SHIMATANI

Assoc. Prof.

Masayuki HENMI

Assoc. Prof.

Hideitsu HINO

Assist. Prof.

Shunichi NOMURA

Assist. Prof.

Masaaki IMAIZUMIProject Researcher

Kazuhei KIKUCHI

Visiting Assoc. Prof. Osamu KOMORI

Project Assoc. Prof. Naoki KAMIYA
Project Researcher

Tsubasa ITO

Visiting Assoc. Prof. Kei TAKAHASHI

Center for Engineering and Technical Support
Director
Deputy Manager

Genta UENO
Yuriko WATANABE

Vice Director

Kazuhiro MINAMI

Unit Leader of Computing Facilities Unit

Mitsuru HAYASAKA

Unit Leader of Computer Networking Unit

Kazuhiro NAKAMURA

Unit Leader of Information Resources Unit

Yuriko WATANABE

Unit Leader of Media Development Unit

Akiko NAGASHIMA

Library
Head

Genta UENO

Administration Planning and Coordination Section
Chief Director
Director of Planning Unit

Hiroe TSUBAKI

Satoshi ITO

Director of Information and Public Relations Unit Satoshi ITO
Director of NOE Promotion Unit

Satoshi YAMASHITA

Director of International Affairs Unit

Tomoko MATSUI

Director of Evaluation Unit

Yoshihiko MIYASATO

Director of Industry-Academia Collaboration
and Intellectual Property Unit

Satoshi YAMASHITA

Director of Gender Equality Unit

Satoshi ITO• URA Station ——————————————————————————————————————————————————————————————————————
Senior URA

Kozo KITAMURA

Chief URA

Keisuke HONDA

Motoi OKAMOTO

Chief URA

Tachikawa Administration Department
Director Isao OSHIRO• General Affairs Division ————————————————————————————————————————————————————————————————————
Manager Tsuyoshi ABE

Deputy Manager
Deputy Manager

Head, General Affairs Team

Masatoshi NAKAMURA

Head, Labor Management Team

Yasunori SAITO

Masatoshi NAKAMURA
Yasunori SAITO

Head, Personnel Team

Yasunori SAITO

27

StaffTachikawa Administration Department• Financial Division ———————————————————————————————————————————————————————————————————
Manager

Katsuhiro OZAKI

Deputy Manager

Koji SAKAMOTO

Head, General Affairs and Audit Team

Koji SAKAMOTO

Head, Budget and Account Settlement Team (NIPR) Michihito SAKURAI

Head, Budget and Account Settlement Team (ISM)

Ichiro KAWAJI

Head, Assets Management and Acceptance Team

Akiko MAEKAWA• Accounting Division ————————————————————————————————————————————————————————————————————
Manager

Yuji TAHARA

Deputy Manager
Deputy Manager

Hiroki TERAUCHI
Masayuki KOBAYASHIHead, Accounting Team

Hiroaki ARAI

Head, Contract Team1

Toshiaki TAKASAKIHead, Contract Team2

Takeshi KUWAHARA

Head, Facilities Team

Takuya SAITO• Research Promotion Division ————————————————————————————————————————————————————————————————————
Manager

Motoki NISHIJIMA

Head, Research Promotion Team

Hideaki ASANO

Head, Graduate School Team

Ayako NARITA

Deputy Manager

Motokazu TOYODA

Head, Cooperative Research Team

Hitoshi HIRAYAMA

Council of The Institute of Statistical Mathematics ( As of April 1, 2019)28

Keiko TAKAHASHI

Director-General, Japan Agency for Marine-Earth Science and Technology ( JAMSTEC) Center
for Earth Information Science and Technology (CEIST )Masahiro MIZUTA

Professor of Information Initiative Center/
Graduate School, Faculty of Information Science and Technology, Hokkaido University

Shigeru OBAYASHI

Director, Institute of Fluid Science, Tohoku University

Nakahiro YOSHIDA

Professor, Graduate School of Mathematical Sciences, University of Tokyo

Masayuki UCHIDA

Professor, Graduate School of Engineering Science, Osaka University

Hiroshi SAIGO

Professor, Faculty of Political Science and Economics, Waseda University

Yasuhiro OMORI

Professor, Faculty of Economics, University of Tokyo

Kikuo MAEKAWA

Professor, Spoken Language Division,
Director, Center for Corpus Development National Institute for Japanese Language and Linguistics

Hideki ASOH

Director, National Institute of Advanced Industrial Science and Technology
Department of Information Technology and Human Factors Artificial Intelligence Research Center Deputy

Mihoko MINAMI

Professor, Department of Mathematics, Faculty of Science and Technology, Keio University

Satoshi ITOProfessor ( Vice Director-General, ISM )Satoshi YAMASHITA

Professor ( Vice Director-General, ISM )Yoshihiko MIYASATO

Professor ( Vice Director-General, ISM )Tomoko MATSUI

Professor ( Director of Department of Statistical Modeling, ISM )Koji KANEFUJI

Professor ( Director of Department of Statistical Data Science, ISM )Satoshi KURIKI

Professor ( Department of Mathematical Analysis and Statistical Inference, ISM )Genta UENO

Professor ( Director of Center for Engineering and Technical Support, ISM )Yoshinori KAWASAKI

Professor ( Director of School of Statistical Thinking, ISM )Ryozo YOSHINO

Professor ( Department of Statistical Data Science, ISM )Kenji FUKUMIZU

Professor ( Department of Mathematical Analysis and Statistical Inference, ISM )Hironori FUJISAWA

Professor ( Department of Mathematical Analysis and Statistical Inference, ISM )Organization

Cooperative Research Committee ( As of June 1, 2019)
Hiroyuki MINAMI

Professor, Information Initiative Center, Hokkaido University

Kunihiko TAKAHASHI

Associate Professor, Department of Biostatistics, Nagoya University Graduate School of Medicine

Takahiro TSUCHIYA

Professor, School of Data Science, Yokohama City University

Toshikazu KITANO

Professor, Department of Architecture, Nagoya Institute of Technology

Nagatomo NAKAMURA

Professor, Department of Economics, Sapporo Gakuin University

Atsushi YOSHIMOTO

Professor ( Director of Department of Statistical Modeling, ISM )Koji KANEFUJI

Professor ( Department of Data Science, ISM )Shiro IKEDA

Professor ( Department of Mathematical Analysis and Statistical Inference, ISM )Junji NAKANO

Project Professor ( School of Statistical Thinking, ISM )Managing Committee of School of Statistical Thinking ( As of April 1, 2019)
Manabu IWASAKI

Dean/Professor, School of Data Science, Yokohama City University

Toshiyasu MATUSHIMA

Director, Center for Data Science, Waseda University

Hiroshi YAMADA

Professor, Graduate School of Social Sciences, Hiroshima University

Seiji YAMADA

Professor, Digital Content and Media Sciences Research Division, National Institute of Informatics

Junichi SHIOZAKI

Senior Manager, Insight Signal Business Department, Nomura Research Institute, Ltd.

Yoshinori KAWASAKI

Director ( School of Statistical Thinking, ISM)

Yukito IBA

Vice Director ( School of Statistical Thinking, ISM)

Satoshi ITOProfessor ( Vice Director-General, ISM)

Kenichiro SHIMATANI

Associate Professor ( Department of Data Science, ISM)

Research Ethics Review Committee ( As of April 1, 2019)
Specialist on epidemiology and social research

Masayuki KANAI

Professor, School of Human Sciences, Senshu University

Specialist on epidemiology and social research

Keiko SATO

Associate Professor, Kyoto University Hospital, Institute for Advancementof Clinical and Translational Science Department of EBM ResearchSpecialist in the field of ethics and law

Hitomi NAKAYAMA

Lawyer, Kasumigaseki-Sogo Law Offices

Person in citizen’s position

Yutaka KURIKI

Kindergarten Director, Nishikokubunji Nursery School

Research education staff of ISM

Tadahiko MAEDA

Associate Professor ( Department of Data Science, ISM )Research education staff of ISM

Yoo Sung PARK

Associate Professor ( Department of Data Science, ISM )Research education staff of ISM

Koji KANEFUJI

Professor ( Department of Data Science, ISM )Research education staff of ISM

Ikuko FUNATOGAWA

Associate Professor ( Department of Data Science, ISM )Research education staff of ISM

Shuhei MANO

Associate Professor (Department of Mathematical Analysis andStatistical Inference, ISM )Professor Emeritus ( As of April 1, 2019)
Sigeki NISIHIRA

Tatsuzo SUZUKI

Giitiro SUZUKI

Ryoichi SHIMIZU

Noboru OHSUMI

Masakatsu MURAKAMI

Kunio TANABE

Tadashi MATSUNAWA

Masami HASEGAWA

Yoshiyuki SAKAMOTO

Takemi YANAGIMOTO

Yoshiaki ITOH

Yasumasa BABA

Katsuomi HIRANO

Masaharu TANEMURA

Makio ISHIGURO

Yosihiko OGATA

Hiroe TSUBAKI

Genshiro KITAGAWA

Nobuhisa KASHIWAGI

Takashi NAKAMURA

Yoshiyasu TAMURA

Tomoyuki HIGUCHI

Junji NAKANO

29

History

1944

June

Based on a proposal submitted at an academic study conference in December 1943, the organization was
founded as an institute under the direct control of the Ministry of Education. This proposal aimed to provide
supervision for studies looking into the mathematical principles of probability and their application, and was
also intended to facilitate, unify and promote the publication of research results.

1947

April

The affiliated statistical specialists’ school was opened.

May

The Institute was divided into the 1st Research Dept. ( fundamental theories ), the 2nd Research Dept. (statisticaltheories for the natural sciences), and the 3rd Research Dept. ( statistical theories for the social sciences ).

1949

June

The Institute was placed under the control of the Ministry of Education because of the enforcement of the Ministryof Education Establishment Law.

1955

September

Reorganized into the 1st Research Dept. ( fundamental theories ), the 2nd Research Dept. ( natural and social
science theories ), and the 3rd Research Dept. ( operations research, statistical analysis theories ). The laboratory
system, comprising 9 laboratories and the research guidance promotion room, was adopted.

1969

October

1971

April

The 4th Research Dept. (informatics theories) was instituted.

1973

April

The 5th Research Dept. (prediction and control theories) was instituted.

1975

October

1979

November

1985

April

Repositioned as a National Inter-University Research Institute due to the regulation change. The new mission
includes providing facilities and skills to other universities, in addition to conducting cutting-edge research on
statistical mathematics. Accordingly, the institute was reorganized into four basic research departments (Fundamental Statistical Theory, Statistical Methodology, Prediction & Control, and Interdisciplinary Statistics ) andtwo strategic centers ( Statistical Data Analysis Center and Statistical Education & Information Center ). The
Statistical Technical Training Center was terminated.

1988

October

The Dept. of Statistical Science was instituted in the School of Mathematical and Physical Science, part of the
Graduate University for Advanced Studies ( SOKENDAI ).

1989

June

The Institute was reorganized as an Inter-University Research Institute based on the National School Establishment Law.

1993

April

The Planning Coordination Chief System was instituted.

1997

April

The affiliated Statistical Data Analysis Center was reorganized into the Center for Development of StatisticalComputing, and the Statistical Education and Information Center was reorganized into the Center for Information
on Statistical Sciences.

2003

September

2004

April

The Institute was reorganized into the Institute of Statistical Mathematics, part of the Research Organi­zation ofInformation and Systems of the Inter-University Research Institute based on the National University Corporation Law. The Planning Coordination Chief System was abolished and the position of Vice Director-General was
instituted instead. The Dept. of Statistical Science in the School of Mathe­matical and Physical Science, SOKENDAI, was reorganized. In addition, the Dept. of Statistical Science and the School of Multidisciplinary Scienceswere instituted.

2005

April

The research organization was reorganized into three research departments ( the Department of Statis­tical
Modeling, the Department of Data Science, and the Department of Mathematical Analysis and Statistical Inference ). The affiliated Center for Development of Statistical Computing, the Center for Information on Statistical
Sciences, and the Engineering and Technical Services Section were inte­grated into the Center for Engineeringand Technical Support. The Risk Analysis Research Center was instituted.

2006

April

The Administration Planning Coordination Unit was instituted.

2008

April

The Research Innovation Center was instituted.

A new office building was constructed in Minato Ward.

The 6th Research Dept. (statistical theories of human behavior) was instituted.
The Information Research Building was constructed.

The Prediction and Knowledge Discovery Research Center was instituted.

The Administration Planning and Coordination Unit was reorganized into the Administration Planning and Coordination Section ( hereafter APCS), within which the Intellectual Property Unit, the Evaluation Unit and the Information and Public Relations Unit were instituted.
2009

30

January

The Planning Unit was instituted within APCS.

October

The Institute was moved to 10-3 Midori-cho, Tachikawa, Tokyo.

2010

June

Officially opened the Akaike Guest House.

July

Reorganized the Administration Office to create the NIPR/ISM Joint Administration Office and launch the General
Service Center. The NOE Forwarding Unit ( now we call “NOE Promotion Unit” ) was instituted within APCS.

2011

January

Research and Development Center for Data Assimilation was instituted.
Survey Science Center was instituted.

2012

January

Research Center for Statistical Machine Learning, Service Science Research Center and School of StatisticalThinking were instituted.

2014

July
December

2017

The URA Station was instituted within the Planning Unit.
The Office of Female Researcher Development was instituted within the Planning Unit.

January

Survey Science Center and Service Science Research Center were closed.

July

Data Science Center for Creative Design and Manufacturing was instituted.

December

The International Affairs Unit were instituted, and the Gender Equality Unit, which had been within the Planning
Unit, reorganized within APCS.
The Intellectual Property Unit was reorganized as the Industry-Academia Collaboration and Intellectual Property
Unit within APCS.

2018

April

Research Center for Medical and Health Data Science was instituted.
The NIPR/ISM Joint Administration Office was reorganized as the Tachikawa Administration Department of
the Research Organization of Information and Systems ( ROIS ).

2019

March

Research and Development Center for Data Assimilation was closed.

31

The Institute of
Statistical Mathematics
Bus stop

to Kamikitadai

Tachikawa City Hall
Bus stop ( Tachikawa Academic Plaza)( TachikawaShiyakusho )Bus stop (Saibansho-mae)
Courthouse

Takamatsu

Local Autonomy
College

National Institutefor Japanese Language
and Linguistics

Tachikawa
Police Station
Disaster
MedicalCenterTama
MonorailShowa Kinen Park

Ome Line

Tachikawa-kita

Tachikawa
Access to the ISM

Chuo Lineto Shinjuku,
Tokyo →

Tachikawa Bus— Tachikawa Academic Plaza bus stop
— 5 min walk from Saibansho-mae
or Tachikawa-Shiyakusho bus stop
Tama Monorail—10 min walk from Takamatsu Sta.
JR Chuo Line
—25 min walk from Tachikawa Sta.

10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan
Tel: +81- ( 0 ) 50-5533-8500 Fax: +81- ( 0 ) 42-527-9302

https://www.ism.ac.jp/